We are looking for an AI Tooling Engineer with strong expertise in JavaScript/TypeScript to help shape how developers and agents build with Supabase.
As part of our engineering team, you'll work across product surfaces like the dashboard assistant, MCP, docs, and developer tooling to make Supabase a seamless platform for AI-native workflows. You'll combine product thinking, strong engineering fundamentals, and an eval-first mindset to build reliable tools, interfaces, and abstractions that serve both our own AI products and the developers building with Supabase.
Client Communication & Support: Manage calls, emails, and general communications, acting as the primary point of contact.
Core Administrative Functions: Efficiently handle critical tasks such as A/R, A/P, work order processing, lease renewals, compliance documents, and detailed email management.
Marketing Assistance: Contribute to client marketing efforts, including social media management.
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Apply strong graphic design principles, including typography, color theory, layout, and visual hierarchy, to craft professional, high-quality digital and print assets aligned to client briefs and brand guidelines.
Develop design assets primarily using Canva and the Affinity Suite, while remaining adaptable across creative software ecosystems, including Adobe.
Prepare and deliver print-ready files across common formats (flyers, brochures, labels, merchandise, and similar collateral), with an understanding of print production standards such as bleed, trim, and color mode requirements.
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Design and build internal AI productivity tools that engineers actually reach for — coding assistants, knowledge retrieval systems, and workflow automation
Develop LLM-powered applications for real engineering use cases: code generation, code understanding, and test automation
Integrate AI capabilities across the full software development lifecycle, from requirements all the way through to deployment
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Design and build internal AI productivity tools that engineers actually reach for — coding assistants, knowledge retrieval systems, and workflow automation
Develop LLM-powered applications for real engineering use cases: code generation, code understanding, and test automation
Integrate AI capabilities across the full software development lifecycle, from requirements all the way through to deployment
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